Linkedin Self Improvement Loop

LinkedIn self-improvement loop

Most "content analytics" is a noun: a report you read once and forget. This is a verb. It runs the build-measure-learn loop on your LinkedIn and keeps state, so every cycle compounds on the last instead of starting from zero.

  1. MEASURE  -> 2. RECONCILE -> 3. UPDATE BELIEFS
  (ingest export)  (did last        (confidence rises if a
        ^           cycle's bet       pattern held, halves if
        |           hold up?)         it broke)
        |                                  |
  6. WAIT  .json` | Parsed metrics from each export, so trends compute across exports (beats the top-50 survivorship trap over time). |

A belief is just: *"posts with this trait beat your average on the chosen metric."* It
starts at low confidence, climbs ~0.34 of the way to 1.0 each cycle it survives, and
halves when a new export contradicts it. Survive enough cycles and it's a law; break and
it's archived.

## What the user downloads (same two files every cycle)

1. **Creator analytics (required)** - `AggregateAnalytics__.xlsx`.
   LinkedIn -> profile -> **Analytics** -> **Export**. Impressions, engagements, top-50
   posts, followers, demographics. (LinkedIn caps it at the top ~50 posts / 365 days.)
2. **Data archive (optional, recommended)** - the `Complete_LinkedInDataExport` zip
   (**Settings -> Data Privacy -> Get a copy of your data -> larger archive**, email,
   ~24h). Its `Shares_*.csv` carries full post text so the loop can tag topics and hooks.

## Step 1: Locate the export

```bash
ls ~/Desktop ~/Downloads 2>/dev/null | grep -iE "AggregateAnalytics|LinkedInDataExport"

Step 2: Run a cycle

cd skills/linkedin-self-improvement-loop
python loop.py \
  --analytics "/path/to/AggregateAnalytics_Name_dates.xlsx" \
  --archive   "/path/to/Complete_LinkedInDataExport_folder" \
  --state ./state \
  --metric engagements        # or impressions | er

--metric picks what the loop optimizes. engagements is the sane default for a personal brand (reach is mostly downstream of engagement + the algorithm). Use impressions only if pure reach is the goal, and read the ER caveat below before you do.

The loop prints its report to stdout and updates ./state. Read the report straight back to the user, in this order: RECONCILE (did last bet hold), PROPOSE (the one experiment), DRAFT BRIEFS.

Step 3: One-off deep snapshot (optional)

For a full one-time report (all the tables, top/bottom posts, correlations) without the loop machinery, run the MEASURE stage directly:

python analyze.py --analytics "...xlsx" --archive "...folder" --out ./out

This writes a styled Excel workbook + tagged CSV. Good for handing a human a static read; the loop is for the recurring improvement cycle.

Step 4: Draft toward the experiment

Take the DRAFT BRIEFS and expand them into real posts. If a drafting skill exists (e.g. social-content), hand it each brief's topic / hook / post_on and let it write in the user's voice. Tag each post mentally with the brief's tests field so next cycle's reconciliation means something. Never auto-post - output drafts, the human ships them.

Step 5: Schedule the next cycle

This is what makes it a loop, not a one-off. After enough posts to measure (~2 weeks), re-run with the next export. Offer to wire it:

/schedule a linkedin-self-improvement-loop run every 2 weeks

Each run tells the user whether the last bet paid off and picks the next one.

Read the numbers honestly (say this every cycle)

• Engagement rate is inversely tied to reach. A 12k-impression post shows a lower ER%

than a 900-impression post with equal raw engagement. The loop's default metric (engagements) sidesteps this; if you switch to er, know it rewards small posts.

• Survivorship bias, fading over time. Any single export is the top ~50 posts only. The

loop's snapshots/ defeat this across cycles, but in cycle 1 a "loss" belief just means "weakest of your winners," not "this bombs."

• Small n. Day-of-week and rare hooks can ride on 3-5 posts. The loop ignores anything

under n=3 and shows n in every row. Treat a 1.6x effect on n=3 as a hint, not a law, until cycles confirm it.

• Engagements is one blended number (no reaction/comment/share split), and native

image/carousel posts usually have no MediaUrl, so the loop can't judge media vs text. Don't fake a conclusion there.

Tuning

Topic and hook detection are two regex dicts at the top of analyze.py (TOPICS, HOOKS), tuned for a B2B / GTM / CRM brand. Edit for a different niche. Loop behavior (learning rate, noise deadband, min sample size, seed confidence) is tunable at the top of loop.py.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NachoLafuente
• Source: NachoLafuente/5050-gtm
• License: MIT

Install and usage instructions live in the source repository linked above.